Power protection and remediation

ABSTRACT

Methods, systems, and apparatus for collect historical power consumption data and power consumption statistics for one or more locations and devices at the location to generate historical power consumption and health data (“historical data”). The historical data are used to develop and provide multiple different protection and monitoring functions. The system may be deployed within a single customer location, e.g., within a building or a plant, and local analytics are developed at the location. Alternatively, the system may be distributed among several locations for a particular customer or multiple different customers and include cloud-based analytics in addition to, or instead of, local analytics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of, and claims priorityto, U.S. patent application Ser. No. 14/942,427, titled “POWERPROTECTION AND REMEDIATION,” filed on Nov. 16, 2015. The disclosure ofthe foregoing application is incorporated herein by reference in itsentirety for all purposes.

TECHNICAL FIELD

This document relates to facilitating power management and protection.

BACKGROUND

Modern electronic equipment is sensitive to power disturbances on thepower grid. Protection systems designed to isolate electronic devicesfrom such disturbances are often used to protect sensitive electronicdevices. While such systems work well for grid disturbances, they do nototherwise address the underlying cause of such disturbances, many ofwhich may be local to a location and not caused by a failure external tothe location.

The problems caused by these disturbances are widespread andmultifaceted. Entire organizations, e.g., maintenance and repair shops,exist largely because of them. Organizations affected by powerdisturbances suffer lost revenue, repair costs, and maintenanceoverhead. Common issues include premature equipment failure, inducederrors, revenue loss, and truck rolls.

With respect to premature equipment failure, the resulting damage fromrepeated exposure to these disturbances wears out and damages thecomponent parts of the electronic equipment. These parts need to bereplaced, typically by trained and experienced technicians, atconsiderable expense. Most modern electronic equipment is made ofmultiple components that need to operate in a reliable, synchronizedmanner. If one or more of the components fails to do so, the typicalresult is an error code and the temporary or permanent unavailability ofthe equipment. This, in turn, leads to down time in an organization andlost revenue.

Finally, a truck roll occurs when a technician has to be dispatched tothe equipment in order to diagnose and address the issue. Often thetechnician is encountered with mysterious ‘No Fault Found’ error codes.The fix typically is something as simple as a power cycle (momentarilycutting the power to the equipment) and allowing the device to reboot.However, the cost of the technician's time and associated overhead(truck, fuel, maintenance, dispatch, et.) can easily exceed severalhundred dollars. Furthermore, if there is a systemic grid problem withina location, it may be very difficult, if not impossible, for the servicetechnician to identify and diagnose.

Finally, the systems described above are often reactive in that theytake protective measures after detection or commensurate with thedetection of a fault, such as a voltage sag or current inrush, and arenot designed to anticipate the need for taking a protective measurebefore a fault condition occurs.

Accordingly, there is a need for proactive protection processes andsystems that in addition to protecting equipment from disturbances,utilizes historical data to detect one or more of systemic topologyproblems, anticipate equipment failure, and adjust protection schemes ona per-device basis.

SUMMARY

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof, for a power customer location that receives power from a powersource, the power customer location including a plurality of electricalloads: receiving by a data processing apparatus and during a reportingtime period, reporting data from each of a plurality of power managementdevices, each power management device coupled to a respective one of theelectrical loads and providing power management for the electrical load,wherein for each power management device the reporting data includes:power characteristics as detected at the electrical load, a time atwhich the power characteristics were detected; determining, by the dataprocessing apparatus, from the reporting data and for each electricalload to which a power management device is coupled, a sensitivityprofile for the electrical load that characterizes the ability of theelectrical load to maintain an operable state in the event of inputpower to the electrical load deviating from a nominal specification; andgenerating, by the data processing apparatus, for each power managementdevice of two or more power management devices, a load-specificprotection specification for the power management device based on thesensitivity profile of the electrical load that is coupled to the powermanagement device, wherein the load-specific protection specification isdifferent from a load-specific protection specification for anther powermanagement device. Other embodiments of this aspect includecorresponding systems, apparatus, and computer programs, configured toperform the actions of the methods, encoded on computer storage devices.

Another innovative aspect of the subject matter described in thisspecification can be embodied in methods that include the actions of:for a power customer location that receives power from a power source,the power customer location including a plurality of electrical loads:receiving, by a data processing apparatus, reporting data from each of aplurality of power management devices, each power management devicecoupled to a respective one of the electrical loads and providing powermanagement for the electrical load, wherein for each power managementdevice the reporting data includes: power characteristics as detected atthe electrical load, and a time at which the power characteristics weredetected; determining, by the data processing apparatus and from thereporting data, historical power characteristics for each electricalload indicative of power consumption when input power is within anominal specification; determining, based on the historical powercharacteristic, that an electrical load operation in a healthy state fora power management device is consuming power at a consumption level thatis a precursor indicator of a malfunction state of the electrical load;and generating, in response to the determination, an alert thatdescribes that the electrical load of the load point may be experiencinga malfunction. Other embodiments of this aspect include correspondingsystems, apparatus, and computer programs, configured to perform theactions of the methods, encoded on computer storage devices.

Another innovative aspect of the subject matter described in thisspecification can be embodied in methods that include the actions of:for a power customer location that receives power from a power source,the power customer location including a plurality of local distributionbranches and wherein each local distribution branch includes a pluralityof electrical loads: receiving reporting data from each of a pluralityof power management devices, each power management device coupled to arespective one of the electrical loads and providing power managementfor the electrical load, wherein for each power management device thereporting data includes: power characteristics as detected at anelectrical load on the distribution branch, and a time at which thepower characteristics were detected; determining, from the reportingdata, a historical power environment profile for the customer locationthat describes historical power characteristics for each of theelectrical loads on the distribution branches; determining, based on thehistorical power environment profile, a combination of electrical loadsthat results in at least a first electrical load operating in a healthystate inducing power-related malfunctions in at least a secondelectrical load; and generating, in response to the determination, analert that describes the combination of electrical loads. Otherembodiments of this aspect include corresponding systems, apparatus, andcomputer programs, configured to perform the actions of the methods,encoded on computer storage devices.

Another innovative aspect of the subject matter described in thisspecification can be embodied in methods that include the actions of:for a power customer location that receives power from a power source,the power customer location including a plurality of local distributionbranches and wherein each local distribution branch includes a pluralityof electrical loads: receiving, during a reporting time period,reporting data from each of a plurality of power management devices,each power management device coupled to a respective one of theelectrical loads and providing power management for the electrical load,wherein for each power management device the reporting data includes:power characteristics as detected at electrical load on the distributionbranch for the electrical load, and a time at which the powercharacteristics were detected; determining, from the reporting data, abaseline power environment profile for the customer location thatdescribes power characteristics on the distribution branches;identifying, based on the baseline power environment profile, adistribution branch within the power customer location for which thepower characteristics indicate a deviation from the baseline powerenvironment profile for at least a threshold deviation period; andgenerating an alert that describes the identified distribution branchand the deviation from the baseline power environment profile. Otherembodiments of this aspect include corresponding systems, apparatus, andcomputer programs, configured to perform the actions of the methods,encoded on computer storage devices.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. The optimization of protection schemes on aper-device basis can increase uptime for devices that are tolerant ofcertain power anomalies. This leads to a reduction in system downtime,nuisance trips, and lost revenue.

Detecting malfunctions based on consumption deviations and not resultingfrom protection circuitry isolating the load allows for proactivedetection of possibly failing loads. Such loads can be proactivelymaintained or replaced, which can eliminate nuisance trips and systemfailures caused by the failing load.

Detecting combinations of loads that are problematic, e.g., a devicethat introduces a series of harmonics when performing an operation andthat cause failures in another device on the same electrical branch,allows for an organization to isolate the devices from each other. Thisreduces electrical wear on the device in which failures were induced,thereby extending the device's life. Additionally, such detection andisolation leads to a reduction in system downtime, nuisance trips, andlost revenue.

Detection of toxic environments within the customer location also leadsto a reduction in system downtime, nuisance trips, and lost revenue.Portions of a local distribution system, such as a branch within abuilding, may exhibit a deviation from a baseline power profile. Such aprofile may include electrical characteristics, failure and/or warningrates, etc. When a deviation in the branch is detected, the system canissue an alert and technicians can begin analyzing the branch todetermine the causes of the deviations and rectify accordingly. Again,such proactive maintenance leads to a reduction in system downtime,nuisance trips, and lost revenue.

The details of one or more embodiments of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an environment which a power protectionsystem may be deployed.

FIG. 2 is a block diagram of an example power management device.

FIG. 3 is a flow diagram of an example process for generating andproviding load-specific protection specifications for power managementdevices.

FIG. 4 is a flow diagram of an example process for determining asensitivity profile for a load on a power management device.

FIG. 5 is a flow diagram of an example process for detectingmalfunctions based on precursor indicators.

FIG. 6 is a flow diagram of an example process for determining toleranceranges for detecting malfunctions.

FIG. 7 is a flow diagram of another example process for determiningtolerance ranges for detecting malfunctions.

FIG. 8 is a flow diagram of an example process for detectingincompatible load combinations.

FIG. 9 is a flow diagram of an example process for determining that afirst load causes a malfunction in a second load.

FIG. 10 is a flow diagram of an example process for detecting a toxicpower environment within a location.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Overview

The systems and methods described in this written description collecthistorical power consumption data and power consumption statistics forone or more locations and devices at the location to generate historicalpower consumption and health data (“historical data”). The historicaldata are used to develop and provide multiple different protection andmonitoring functions. The system may be deployed within a singlecustomer location, e.g., within a building or a plant, and localanalytics are developed at the location. Alternatively, the system maybe distributed among several locations for a particular customer ormultiple different customers and include cloud-based analytics inaddition to, or instead of, local analytics.

In operation, power management devices are distributed in a customerlocation. The customer location receives power from a power source andincludes local distribution branches, each of which has one or moreelectrical loads. Each power management device is interposed between anelectrical outlet and a load, and provides power protection, e.g.,voltage and surge protection, undercurrent protection, load faultprotection, and so on, with respect to the electrical load.

Each power protection device is also in data communication with a dataprocessing apparatus. The data processing apparatus may be a singlecomputer, or a network of computers, and maybe located locally at thelocation, or in the cloud. The data processing apparatus receivesreporting data from each of the power management devices. The reportingdata includes, for each power management device, power characteristicsas detected at the electrical load on the distribution branch for theelectrical load, and a time at which the power characteristics weredetected. The power characteristics may include a voltage level, acurrent level, lag or lead measures, harmonic detections, or any otherdata that can be observed and collection at the connection point of thepower management device. Furthermore, the power management device mayalso specify, in the reporting data, the source of the powercharacteristics, e.g., whether the power characteristics are observed onthe distribution branch, or observed on the load connected to the powermanagement device, and whether the load or the distribution branchappears to be the source of any disturbances observed.

The reporting data are used to generate historical data, and from thehistorical data various models and profiles may be generated. The modelsmay be used to predict certain events that may occur within a location,and the profiles may be used to by the power management devices tomodify the requirements for taking a protective action for a particularload connected to a particular power management device. Examples of suchuse of the historical data include the optimization of protectionschemes on a per-device basis, detecting malfunctions based onconsumption deviations, detecting combinations of loads that areproblematic, and detecting toxic environments within the customerlocation.

With respect to the optimization of protection, in one implementation,the data processing apparatus determines, from the reporting data andfor each electrical load to which a power management device is coupled,a sensitivity profile for the electrical load. The sensitivity profilefor each electrical load characterizes the ability of the electricalload to maintain an operable state in the event of input power to theelectrical load deviating from a nominal specification. Based on eachsensitivity profile, the data processing apparatus generates aload-specific protection specification for the power management device.The load-specific protection specification is optimized according to anoptimization constraint for the electrical load.

With respect to detecting malfunctions based on consumption deviations,in one implementation, the data processing apparatus determines, fromthe reporting data and for each electrical load, historical powercharacteristics indicative of power consumption when input power iswithin a nominal specification. Then based on the historical powercharacteristics, the data processing apparatus can determine whether anelectrical load operating in a healthy state for a power managementdevice is consuming power at a consumption level that is a precursorindicator of a malfunction state of the electrical load. If such adetermination is made, then the data processing apparatus generates analert that describes that the electrical load of the load point may beexperiencing a malfunction.

With respect to detecting combinations of loads that are problematic,the data processing apparatus determines, from the reporting data, ahistorical power environment profile for the customer location thatdescribes historical power characteristics for each of the electricalloads on the distribution branches. Based on the historical powerenvironment profile, the data processing apparatus determines acombination of electrical loads of two or more different types thatresult in at least one of the electrical loads operating in a healthystate inducing power-related malfunctions in at least another electricalload. Upon such a determination, the data processing apparatus generatesan alert that describes the combination of electrical loads.

With respect to detecting toxic environments within the customerlocation, the data processing apparatus determines, from the reportingdata, a baseline power environment profile for the customer locationthat describes power characteristics on the distribution branches. Thenthe data processing apparatus identifies, based on the baseline powerenvironment profile, a distribution branch within the power customerlocation for which the power characteristics indicate a deviation from abaseline power environment profile for at least a threshold deviationperiod. If the data processing apparatus determines that the indicateddeviation from the baseline power environment profile is attributed tothe electrical loads on the identified distribution branch, the dataprocessing apparatus then generates an alert that describes theidentified distribution branch and the deviation from the baseline powerenvironment profile.

The above example implementations are not exhaustive of the variousintelligence power protection diagnostics, treatment and immunizationsthat can be realized. The implementations described above and additionalfeatures are described in more detail in the sections that follow.

Example System Implementation

FIG. 1 is a block diagram of an environment 100 which a power protectionsystem may be deployed. The environment 100 includes a power source 102.The power source 102 provides power to one or more customer locations110. The power source 102 may be a utility grid, or a combination of autility grid and addition energy sources, such as renewable energysources.

Each customer location 110 receives one or more phases (4) from thepower source 102. For example, a residential customer location or asmall office building may have a single phase as mains power, while alarger customer location, such as an industrial plant or large officebuilding, may receive three phase power from the power source 102. Inputvoltages and input power capacity may vary for each location 110.

Each location receives power from the power source 102 through a maindistribution panel 112. Branch circuits 114A . . . N distribute powerfrom the main distribution panel 112 through the location 110. Theexample distribution system shown in FIG. 1 is a simplifiedrepresentation, and is not exclusive of additional distributioncircuits, such as step down transformers, additional power panels andbranch circuits, AC to DC conversion, and so on.

Each branch 114 is connected to power management devices 120, which, inturn, are coupled is to a respective electrical loads 121 and providepower management for the electrical load 121. In addition to variouscurrent and voltage protection measures, each power management device120 can provide one or more of the protection measures described in thesections below. A more detailed description of a power management device120 is provided with reference to FIG. 2 below.

Each power management device 120 includes a communication subsystem theenables the device 120 to communicate with a data processing apparatus,such as a computer 130. As shown, the computer 130 is located within thelocation 110. The computer 130 receives reporting data from the powermanagement devices 120 through communication links 118 (which may bewired or wireless) and stores the reporting data as historical data 134.From the historical data 134, the computer 130, executing a powermanager process 132, generates various models 136 and/or profiles 138,as will be described in more detail below. The computer 130 may processdata for only the location 110, or, alternatively, may processadditional data from other locations 110 that are associated with thecustomer.

In some implementations, the functionality of the computer 130 may beintegrated into one of the power management devices 120. In operation,the power management devices 120 may discover each other and select oneof the power management devices 120 to act as a master device 120 thatincorporates the functions of the computer 130 described below. Anynumber of appropriate selection algorithms can be used to select themaster device 120. One example selection algorithm is a latencyalgorithm that selects the device 120 with the lowest average latencywhen communicating with all other devices 120 relative to the averagelatency determined for each other device 120.

In some implementations, the functionality of the computer 130 can bedistributed in the cloud, as indicated by the computer 140 and network104. When distributed in the cloud, the computer 140 may process dataonly for the location 110, or, alternatively, may use reporting datafrom multiple other locations 110 as well. In the case of the latter,resulting models 146 and profiles 148 may be more robust, as thehistorical data 144 includes data from many different locations, and thepower manager process 142 may thus learn the models 146 and profiles 148from a larger data set.

This historical data 134 (and/or 144) includes power characteristicsreceived from each power management device 120. The powercharacteristics may include, for each data set reported, a voltagelevel, a current level, lag or lead measures, harmonic detections, orany other data that can be observed and collected at the connectionpoint of the power management device 120.

Reporting capabilities of each power management device 120 are furtherdescribed with reference to FIG. 2, which is a block diagram of anexample power management device 120. In the example implementation showin FIG. 2, the power management device includes a processor 202, amemory 204, and I/O circuitry 206. The memory 204 stores models 136 andprofiles 138 that are provided from the power manager process 132. Theprocessor 202 performs operations pursuant to the models 136 andprofiles 138 stored in the memory 204 and that are responsive tomeasurements detected by line monitors 210 and 212.

The line monitors 210 monitor power characteristics, e.g., voltage,current, harmonics, etc., as seen at each load 220 that is connected tothe power management device 120. Likewise, the line monitor 212 monitorspower characteristics, e.g., voltage, current, harmonics, etc., as seenat outlet of the branch to which the power management device 120 isconnected. The power management device 120 may also specify, in thereporting data, the source of the power characteristics, e.g., whetherthe power characteristics are observed on the distribution branch byline monitor 212, or observed on the load connected to the powermanagement device by line monitor 210, and whether the load or thedistribution branch appears to be the source of any disturbancesobserved. For example, if the data received from the line monitors 210and 212 indicate that there is a current inrush that is followed by avoltage sag, then the processor 202 may determine that the load 220 isthe cause of the disturbance. Conversely, if the data received from theline monitors 210 and 212 indicate that there is a voltage sag followedby a current rush, then the processor 202 may determine that the causeof the disturbance is external to the load 220, e.g., from a disturbancethat is received from the distribution branch 114 to which the powermanagement device is connected.

In some implementations, the power management device 120 may identifythe types of electrical loads 121 that are connected to it. Theelectrical load 121 may be manually identified to the power managementdevice 120 by a system administrator through a user interface served bythe computer 130, or may be detected automatically, such as by use of aMachine Information Byte (MIB) received over the connection between theI/O circuitry 206 and the load 220.

In other implementations, the power management device 120 may includeprofile data that specifies observed power characteristics that areindicative of certain loads to generate equipment fingerprints. Theobserved power characteristics can be learned from the historical data134 and/or 144 by any appropriate machine learning process that canmodel and identify information bearing signals emergent from large datasets. For example, from the historical data 144, the power managerprocess 142 may determine that several instances of a particular loadtype have been identified to power management devices 120. Assume thatthe particular load type is a particular model of a photocopy machine.The power manager 142 may process the reporting data for each instanceof the particular model of the photocopy machine received from multiplelocations. Power characteristics that are unique and consistent to theparticular model of the photocopy machine are then detected and storedas a profile unique to that device. The power characteristics must beunique so that they may be used to identify the particular device, andmust be consistent across the reporting data for the devices. In thecase of the latter, for example, some devices may have faulty or failingequipment, resulting in power characteristics that are inconsistentrelative to the entire set of identified devices. Such inconsistent dataare not used for equipment fingerprinting.

Once generated, the profile may be distributed to each power managementdevice 120 so that the device 120 can identify the copier, should thecopier be connected to it. The identification may be subject to operatorconfirmation through a user interface served by the computer 130 orcomputer 140.

In other implementations, the power manager process 142 may determinemultiple instances of unique and consistent power characteristics andgenerate a profile for an unidentified unique device and distribute theprofile to the power management devices. A power management device 120then identifies the unique device by the observed power characteristicsmay then send a signal to the computer 130, which, in turn, may cause auser device to prompt an administrator to identify the unique device.Once data is received that identifies the unique device, the data may bedistributed locally to other power management devices 120 in thelocation 110, and may also be communicated to the cloud computer 140.Once received in the cloud, the profiles 148 may be updated anddistributed to other power management devices 120 at other locations. Inthis way, electrical loads that do not have the capability to identifythemselves to the power management devices 120 may nevertheless bediscovered and identified by means of signal analysis and useridentification.

Reporting data may also include sensor data from sensor(s) 214. Thesensors data may include temperature and humidity, for example, whichmay also be used to model disturbances and particular loads.

The processor 202 drives switches 208 in response to observed powercharacteristics, models 136, and profiles 138. As will be described inmore detail below, the use of the models 136 and profiles 138facilitates multiple different proactive protection and managementschemes.

A battery device 214 may be included in the power management device 120to provide power to the device 120 in the event of an outage. By use ofthe battery device 214, the power management device 120 may stillprovide reporting data and communicate with other devices.

Returning now to FIG. 1, each location may also have loads 123 that arenot connected to power management devices. Thus, in someimplementations, power meters 115 may be connected to each branch andthe power meter readings may be reported to the computer 130 forprocessing by the power manager 132. The additional information providedby the power meters 115 can thus be used to model each branch and detectelectrical loads on each branch that are not protected by the powermanagement devices 120.

Additionally, sensor data from sensor(s) 116 located throughout thelocation 110 may report environmental conditions, such as temperatureand humidity. The environmental conditions may also be used for modelingand profile generation, as some devices are susceptible to temperatureand humidity changes.

Additional data 128 may also be collected by the computer 130, such asthe time, date, and weather. The data may be observed by sensors orreceived from an external service, such as a feed that providesweather-related data, lightning detection, etc. The data 128 may be usedto further tune the models 136 and profiles 138, and the data 128 may beprovided to the power management devices 120 that are operatingaccording to such models 136 and profiles 138. For example, a particularload 121 may be highly susceptible to power surges, and thus, duringlightning events, such as period of a thunderstorm warning, the load 121may be proactively disconnected from a branch 114 until the thunderstorm warning expires.

The distribution topology of the location 110 can, in someimplementations, be specified by system administrators. For example, amapping of the distribution grid within the location may be provided toeach power management device 120, and each device 120 may also beprovided with information that describes its respective location on thedistribution grid. Alternatively, in some implementations, the reportingdata provided by the power management devices 120 to the computer 130may be used to derive the distribution grid. For example, if only aproper subset of power management devices 120 in a location 110simultaneously experience an outage or a particular disturbance, thepower manager 132 may determine that those devices are on a particularbranch. Model data 146 describing the topology and device 120distribution within the topology may then be updated and distributed tothe devices 120.

As described above, the data gathered by the power management devices120 and processed by the power manager process 132 (and/or 142) can beused to facilitate a variety of intelligent power management schemes.Examples of several such schemes are described in detail in thefollowing sections.

Load Specific Protection Specification

Different machines react differently to power disturbances. In manycases, some makes or models of a particular piece of equipment may beless sensitive to a particular disruption than other machines. Using theinformation collected about the power environment of a location 110, andthe performance characteristics of the loads, the power manager process132 (and/or 143) can generate load-specific protection specificationsfor power management devices. For example, a particular piece ofequipment, from the reporting data, may be determined to be tolerant ofa voltage sag that is well below a nominal specification, e.g., up to10% below the nominal minimum voltage. Since the equipment is moretolerant to brownouts, the power manager process 132 can generate aprofile for the power management device 120 that is protecting theequipment that causes the power management device 120 to maintain powerto the equipment even when the input voltage is below a nominalspecification, e.g., up to 10% below the nominal specification.

FIG. 3 is a flow diagram of an example process 300 for generating andproviding load-specific protection specifications for power managementdevices. The process 300 is described with reference to the computer130, but process steps involving the computer 130 may also be performedby the cloud-based computer 140.

The process 300 receives, during a reporting time period, reporting datafrom each power management device of a set of power management devices(302). The reporting data includes, for each power management device120, power characteristics as detected at the electrical load on thedistribution branch for the electrical load, and a time at which thepower characteristics were detected. The data rate for the reportingdata may vary. For example, during normal operation, each device 120 maystore reporting data in the memory 204 and only report data every nseconds. However, in response to a disturbance, each device may thenreport data at a much higher rate, e.g., every n milliseconds, and mayalso send reporting data for a time period leading up to thedisturbance. The computer 130 stores the reporting data in thehistorical data 134.

In some implementations, the reporting data may also includeenvironmental data, such as temperature and humidity. The environmentaldata may be provided by the power management devices 120, or may beprovided by sensors 116, or by some other source.

The process 300 determines, from the reporting data and for eachelectrical load to which a power management device is coupled, asensitivity profile for the electrical load (304). The sensitivityprofile for the electrical load characterizes the ability of theelectrical load to maintain an operable state in the event of inputpower to the electrical load deviating from a nominal specification. Thepower manager 132 can derive the sensitive profile for each load bycomparing the power characteristics observed for the load duringdisturbances. If the characteristics indicate the load maintains ahealthy operational state, e.g., the load does not trip, or the loaddoes not draw an inrush that is determined to be excessing during avoltage sag, the power manger process 132 may determine that the load istolerant of the corresponding disturbances experienced. Alternatively,if the load provides data describing its operational health to the powermanagement device, e.g., by means of a USB connection, for example, thepower manager 132 may use such data to derive the sensitivity profile.The sensitivity profile may also take into account the environmentaldata. One example process for deriving a sensitivity profile isdescribed with reference to FIG. 4 below.

The process 300 generates, for each power management device, aload-specific protection specification for the power management devicebased on the sensitivity profile of the electrical load that is coupledto the power management device (306). The profile is optimized accordingto an optimization constraint for the electrical load. For example,assume a standard protection specification causes the power managementdevice 120 to isolate a load if the input voltage is outside of anominal specification of 120V+/−5V. However, if the optimizationconstraint is to increase uptime, and the load on the power managementdevice has a sensitivity profile that indicates the load performs wellfor voltage sags as low as 100V, then the load-specific protectionspecification may specify that the power management device 120 isolate aload if the input voltage is outside of a range of 100V to 125V.

The process 300, for each power management device, provides theload-specific protection specification generated for the powermanagement device (308). For example, in FIG. 1, each power managementdevice 120 will receive load-specific protection specificationparticular to each load connected to the device 120. Thus, a powermanagement device 120 with two different loads connected to it mayreceive two different protection specifications. Thereafter, each powermanagement device 120 will monitor the input power at the electricalload and determine whether the input power is experiencing an inputpower disturbance that requires, pursuant to the load specificprotection specification for the load, a protection action. Thus,depending on the protection specifications, for a particular powerdisturbance one power management device may determine that an electricalload requires a protection action, and another power management devicemay determine that an electrical load does not require a protectionaction.

FIG. 4 is a flow diagram of an example process 400 for determining asensitivity profile for a load on a power management device. The process400 can be implemented in the power manager process 132, or by thecloud-based power manager process 142.

The process 400 determines, for each power characteristic at eachcorresponding time, an operational state of the electrical load at thecorresponding time (402). The operational states include a healthy stateand a malfunction state, and may be determined according to thetechniques described above.

The process 400 determines, from the operational sates at thecorresponding times, transitions from a healthy state to a malfunctionstate and a set of power characteristics at corresponding times for thetransitions (404). For example, a particular load, according to thereporting data, may have transitions from a healthy state to amalfunction state in response to some disturbances, but may otherwisemaintain a healthy state in response to other disturbances. The powermanager process 132 may determine, from transition times, thecorresponding power characteristics for each transition.

The process 400 determines, for each transition from a healthy state toa malfunction state, whether the power characteristics in the set ofpower characteristics are indicative of a cause of the transition (406).In the example above, assume that the load begins to experience a largeinrush when the input voltage drops below 100V, but otherwise maintainsa nominal input current when the voltage is above 100V. Assuming noother data are available, the power manager process 132 would identifyan input voltage below 100V as being a cause of the transition.

Now assume that for some transitions certain harmonics were present inthe input voltage. The power manager process 132 may initially determinethat the harmonics are not the cause of the transition, as they are notpresent for each transition. The power manager process 132 may furthersearch for the presence of the harmonics at other times, such as whenthe input voltage is above 100V, and if the harmonics do not positivelycorrelate to the transitions, then the harmonics are not identified asbeing a cause of the transition.

The process 400 determines the sensitivity profile based on the powercharacteristics that are indicative of causes of the transitions (408).For example, based on the findings describe above, the power mangerprocess 132 will determine that the load is sensitive to a voltage sagbelow 100V.

The sensitivity profile may also take into account environmental data,and adjustments may be made based on environmental factors. For example,the power manager process 132 may also determine that when thetemperature is over 75 degrees Fahrenheit, the voltage at which afailure occurs increases linearly with the temperature. Accordingly, fortemperatures above 75 degrees, the voltage sag limit may increaselinearly based on the observed relation.

Consumption Deviation Detection

In addition to remediation during disturbances, detection of potentialfailures during nominal power conditions can also be performed. Forexample, the power manager system 132 can detect the energy consumptionof electronic equipment during a reporting period. The reporting periodis long enough to gather enough data to model typical consumption of theequipment. Thereafter, variances from the model for the equipment can bereported to a responsible party as a possible need for action.

For example, a particular piece of equipment is drawing very little orno energy when its corresponding model indicates the equipment should bedrawing a full load. A responsible party can be alerted that theequipment is offline. Conversely, if a particular piece of equipment isdrawing significantly more energy than its model indicates it should bedrawing, then the equipment may be distressed and nearing a failure. Amessage can be sent to a responsible party to perform proactivemaintenance before an outage at an inopportune time occurs.

FIG. 5 is a flow diagram of an example process 500 for detectingmalfunctions based on precursor indicators. The process 500 can beimplemented in the power manager process 132 (or 142) and the powermanagement devices 120.

The process 500 receives, during a reporting time period, reporting datafrom each power management device of a set of power management devices(502). As described above, the reporting data includes, for each powermanagement device 120, power characteristics as detected at theelectrical load on the distribution branch for the electrical load, anda time at which the power characteristics were detected. Environmentdata may also be received.

The process 500 determines, from the reporting data, historical powercharacteristics for each electrical load on the distribution branchesindicative of power consumption when input power is within a nominalspecification (504). For example, for periods of time when there are nopower disturbances, the power consumption for each load 121 connected toa power management device 120 can be modeled. The models may then bedistributed to the power management devices 120. Alternatively, thepower manager process 132 may retain the models.

The process 500 determines, after the reporting time period and based onthe historical power characteristic, that an electrical load operatingin a healthy state for a power management device is consuming power at aconsumption level that is a precursor indicator of a malfunction stateof the electrical load (506). In the case of the models beingdistributed to each power management device, the decision may be made ateach power management device 120. Conversely, if the power managerprocess 132 retains the models, the power manager model may receiveadditional reporting data and determine that a particular device isdeviating from a normal consumption level. This can be interpreted as aprecursor indicator of a malfunction state of the electrical load, or,alternatively, that the electrical load has already malfunctioned.

The process 500 generates, in response to the determination, an alertthat describes that the electrical load of the load point may beexperiencing a malfunction. (508). For example, power management device120, or the power manager process 132, may generate a text alert thatdescribes the particular equipment and the power management device towhich it is connected, and the particular deviation. The text alert maybe sent to a technician to inform the technician that maintenance may berequired.

Because a particular piece of equipment may vary its load during certainoperations and certain times of day, the power manager process 132derives a set of tolerance ranges within which the power characteristicsare determine to indicate expected consumption. For example, a copiermachine may experience inrush during a copy operation, and may also drawextra load during a cooling operation for several minutes after longcopy operation. Over time, the performance of load is modeled astolerance ranges, and as long as the power characteristics indicate theload is within the tolerance ranges when the input power is within anominal specification, the load is determined to be healthy.

FIG. 6 is a flow diagram of an example process 600 for determiningtolerance ranges for detecting malfunctions. The process 600 can beimplemented in the power manger process 132 or 142, or within each powermanagement device 120.

The process 600 determines, for each of a plurality of powercharacteristics, each at corresponding times, an operational state ofthe electrical load at the corresponding time (602). The operationalstates include a healthy state and a malfunction state, and may bedetermined according to the techniques described above.

The process 600 determines, for the healthy state, a set powercharacteristics that are indicative of the healthy operational statewhen input power is within the nominal specification (604). For example,with reference to the copier machine, the power manager process 132 maydetermine a maximum inrush current and a maximum duration for the inrushcurrent based on historical data. The power manager process 132 may alsodetermine that after the inrush, the copy machine typically draws acertain amount of current, e.g., 7A, and lags the voltage by no morethan certain lag amount.

The process 600 determines, from the set of power characteristics, a setof tolerance ranges for the set of power characteristics (606). Forexample, the power manager process 132 may determine deviations from thevalues determined above. The deviations may take into accountenvironmental conditions, such as temperature and humidity, anddifferences in nominal input voltages.

Tolerance ranges may also be determined from observed malfunctions andfailures that occur when the input power is within a nominalspecification. Because the input power is within the nominalspecification, the failures can be attributed to a failure within thefailed equipment. The performance of the equipment leading up to thefailure can thus be examined to determine tolerance ranges formonitoring. This process is described with reference to FIG. 7, which isa flow diagram of another example process 700 for determining toleranceranges for detecting malfunctions.

The process 700 determines, for each power characteristic of pluralityof power characteristics at corresponding times, an operational state ofthe electrical load at the corresponding time (702). The operationalstates include a healthy state and a malfunction state, and may bedetermined according to the techniques described above.

The process 700 determines, from the operational states at thecorresponding times, transitions from a healthy state to a malfunctionstate when input power is within a nominal specification (704). Becausethe input power is within the nominal specification, the failures can beattributed to a failure within the failed equipment.

The process 700 determines, for each transition from a healthy state toa malfunction state, whether the power characteristics of thecorresponding times of the transition are indicative of the transition(706). The determination of whether the power characteristics of thecorresponding times of the transition are indicative of the transitioncan be made by any appropriate method. For example, assume the dataindicates there are four copy machines on a same branch, and thus eachreceives a same power input. Assume that one of the copy machines failedwhile power input was within a nominal specification. Power consumptioncharacteristics of the failed copy machine are compared to the powerconsumption characteristics of the remaining three copy machines thatdid not fail. The comparison yields, for example, that during a copyoperation, the inrush current distribution of the failed copy machinediffers from the inrush of the other copy machines in that its peakduration lasts several milliseconds longer than the longest inrushduration of the other copy machines that did not fail, e.g., 12milliseconds for the machine that failed as compared to 7 millisecondsto the machines that did not fail. Thus, the power manager process 132may determine that a peak inrush that lasts longer than a maximum peakinrush duration of the other copy machines that did not fail may be aprecursor signal for equipment failure.

The process 700 determines the tolerance ranges for the set of powercharacteristics based on the power characteristics that are determinedto be indicative of the transitions (708). For example, based on theabove inrush observations, the power manager process 132 may determinethat a peak inrush duration of 9 milliseconds or longer may be a signalthat is precursor indicator of a malfunction.

Incompatible Load Combination Detection

Some types of equipment that draw significant loads, e.g., vendingmachines, vacuums, etc., introduce power disturbances into theenvironment. These disturbances may be in the form of an inrush current,voltage sag, harmonics, and so on. While the equipment may itself beoperating normally, it may nevertheless impact other equipment connectedto the same branch. By monitoring the power environment on the branchthe power manager process 132 can detect when such incompatible loadcombinations are present. This information can be used to inform aresponsible party that action should be taken, and/or adjust theprotection specifications of the surrounding power management devices120.

For example, a vending machine and other office equipment are located onthe same branch circuit. The vending machine introduces a voltage sageach time its compressor turns on, negatively impacting the surroundingequipment. The power manager process 132 detect these devices by theircharacteristic disturbance signatures and issue an alert to aresponsible party.

By way of another example, assume a new electronic load is introducedonto a branch. The additional load on the branch causes an increase insags and brownouts due to the branch's inability to fully handle theadded load. Again, the power manager process 132 detect these devices bytheir characteristic disturbance signatures and issue an alert to aresponsible party.

FIG. 8 is a flow diagram of an example process 800 for detectingincompatible load combinations. The process 800 can be implemented inthe power manger process 132, or by the cloud-based power managerprocess 142.

The process 800 receives, during a reporting time period for a customerlocation, reporting data from each of a plurality of power managementdevices (802). As described above, the reporting data includes, for eachpower management device 120, power characteristics as detected at theelectrical load on the distribution branch for the electrical load, anda time at which the power characteristics were detected. Environmentdata may also be received.

The process 800 determines, from the reporting data, a historical powerenvironment profile for the customer location that describes historicalpower characteristics for each of the electrical loads on thedistribution branches (804). For example, over a period of time, anumber of electrical loads on a particular branch appear to be operatingin a consistently healthy state. The electrical loads include serversand lighting. However, at a certain point in time, a copy machine isadded to the branch. While the branch has an overall current rating thatmore than adequately supports the load connected to it, over timeseveral computers begin to experience power-related malfunctions. Thereporting data will capture the performance of the loads before andafter the addition of the copy machine in the historical powerenvironment profile.

The process 800 determines, based on the historical power environmentprofile, a combination of electrical loads that result in at least oneof the electrical loads operating in a healthy state inducingpower-related malfunctions in at least another electrical load (806).For example, based on the data above, the power manager process 132 willdetermine that the copy machine on the same branch as the computers iscausing power-related malfunctions in the computers.

The process 800 generates, in response to the determination, an alertthat describes the combination of electrical loads (808). For example,the power manager process 132 may generate a text alert that describesthe combination and branch circuit, and that the combination is inducingpower-related malfunctions of certain equipment. The alert may be sentto a technician to inform the technician that remediation, e.g.,relocating the copy machine to another branch, may be required.

FIG. 9 is a flow diagram of an example process 900 for determining thata first load causes a malfunction in a second load. The process 900 canbe implemented in the power manger process 132, or by the cloud-basedpower manager process 142.

The process 900 determines, for each electrical load, operational statesof the electrical load at corresponding times (902). The operationalstates include a healthy state and a malfunction state, and may bedetermined according to the techniques described above.

The process 900 determines, for each electrical load and from theoperational states at the corresponding times, transitions from ahealthy state to a malfunction state (904). For example, for each loadon a particular branch, the power manager process 132 will identifytimes, if any, the load transitioned from a healthy state to amalfunction state.

The process 900 determines, for each transition from a healthy state toa malfunction state, respective sets of power characteristics at thecorresponding times for the transitions (906). For example, assume thatfor a particular branch, several computers failed at certain times afterthe copy machine was added to the branch. For each identified time, thepower characteristics of each device on the branch are determined.

The process 900 compares the respective sets of power characteristics atthe corresponding times for the transitions to each other (908).Continuing with the above example, assume that the copy machine has alarge inrush, and that each failure is coincident with the copy machinedrawing a large inrush current. The power manger process 132 will thusdetermine there is a positive correlation between the inrush on thebranch and the failures in the computers. Accordingly, the power mangerprocess 132 may generate an alert detailing the incompatiblecombination.

Toxic Environment Detection

Power disturbances in a location may not be attributable to anyparticular combination of equipment, or the combination may not bedetectable by the power manager process 132. However, the power mangerprocess 132 can still process historical data 134 and detect when powerdisturbances are attributable to the electrical loads on the branch, andnot due to some external factor. For example, based on historical data,the power manager process 132 may create a baseline “normal” powerenvironment profile, and then monitor for deviations from the baseline.The power manger process 132 may then notify a technician of thedetected deviations so that the technician may begin troubleshooting toidentify causes of the disturbances.

One example process for detecting toxic environments is described withreference to FIG. 10, which is a flow diagram of an example process 1000for detecting a toxic power environment within a location. The process1000 can be implemented in the power manger process 132, or by thecloud-based power manager process 142.

The process 1000 receives, during a reporting time period, reportingdata from each of a plurality of power management devices, each powermanagement device coupled to a respective one of the electrical loadsand providing power management for the electrical load (1002). Asdescribed above, the reporting data includes, for each power managementdevice 120, power characteristics as detected at the electrical load onthe distribution branch for the electrical load, and a time at which thepower characteristics were detected. Environment data may also bereceived.

The process 1000 determines, from the reporting data, a baseline powerenvironment profile for the customer location that describes powercharacteristics on the distribution branches (1004). For example, thebaseline power environment profile may include a baseline rate ofelectrical disturbances for each distribution branch. Each electricaldisturbance is a deviation of power as measured on the distributionbranch from a nominal specification, e.g., a voltage sag or spike, anover current, etc. The baseline power profile may also include abaseline rate of protective actions taken by power management devices120 on a distribution branch, and a baseline rate of equipmentmalfunctions on a branch. Other data can also be recorded in thebaseline power environment profile.

The process 1000 identifies, based on the baseline power environmentprofile, a distribution branch within the power customer location forwhich the power characteristics indicate a deviation from the baselinepower environment profile for at least a threshold deviation period(1006). For example, there power manager process 132 may determine thata distribution branch is beginning to experience electrical disturbancesat a rate higher than the electrical disturbance rate in the baselinepower environment profile over the period of a workday. Likewise, anincrease in protective actions or equipment malfunctions that indicatean increased rate of disturbances may also be detected.

The process 1000 determines that the indicated deviation from thebaseline power environment profile is attributed to the electrical loadson the identified distribution branch (1008). For example, the powermanager processor 132 may determine that the deviation is attributed tothe electrical loads on the identified distribution branch by monitoringdeviations on other branches. If the other branches do not exhibitdeviations from their respective baseline power environment profiles,then the cause of the disturbance increase is likely isolated to thebranch. Likewise, the power manager process 132 may determine whetherthe power source 102 of the location, e.g., the grid, is the cause ofdeviation. For example, brown outs reported in the grid, or observed bythe power manager process 132, may result in the power manger device 132not attributing failures in a branch to the loads on the branch.

The process 1000 generates, in response to the determination that theindicated deviation is attributed to the electrical loads, an alert thatdescribes the identified distribution branch and the deviation from thebaseline power environment profile (1010). The alert may be routed to atechnician for diagnosis and troubleshooting.

Additional Implementation Details

The features described above are not exhaustive and other protection anddiagnosis schemes can also be implemented. For example, in the case of asustained over voltage or brownout, when a particular devices is in acritical state, e.g., in the middle of applying a software update, asudden loss of power due to a protection process can be detrimental andcause an outage of the equipment. Thus, in some implementations, thepower management device 120 can warn the electrical load 121 of apending outage and allow the load to achieve a state where the outagewill not be detrimental. The load can then instruct the device 120 whenit can be isolated. Likewise, the load 121 can inform the device 120that it is in a delicate state and that power should not be cut.Communication between the electrical load 121 operating system and thedevice 120 can accomplished by APIs or any other appropriate mechanism.

By accessing historical data from multiple different locations, a cloudbased power manager process 142 can allow facilities managers to runscenarios to predict power disturbances. For example, a facilitiesmanager may be tasked with installing several copy machines for a tenanton a floor in a building. Using machine learned models generated fromthe historical data 144, the power manager processor 142 can predictimpacts resulting from the addition of the copy machines onto existingbranches, and even generate suggested placements of the copy machineswithin the facility to minimize impacts to existing equipment.

In addition to temperature and humidity, other environment data such assound levels and motion measurements may be used. For example, a suddenincrease in a high pitched noise in a server room, coupled with a slightincrease in current draw for a server rack, can be correlated toindicate a failing fan motor.

Embodiments of the subject matter and the operations described in thisspecification can be implemented in digital electronic circuitry, or incomputer software, firmware, or hardware, including the structuresdisclosed in this specification and their structural equivalents, or incombinations of one or more of them. Embodiments of the subject matterdescribed in this specification can be implemented as one or morecomputer programs, i.e., one or more modules of computer programinstructions, encoded on tangible computer storage medium for executionby, or to control the operation of, data processing apparatus. Acomputer storage medium can be, or be included in, a computer-readablestorage device, a computer-readable storage substrate, a random orserial access memory array or device, or a combination of one or more ofthem. Moreover, while a computer storage medium is not a propagatedsignal, a computer storage medium can be a source or destination ofcomputer program instructions encoded in an artificially generatedpropagated signal. The computer storage medium can also be, or beincluded in, one or more separate physical components or media (e.g.,multiple CDs, disks, or other storage devices).

The operations described in this specification can be implemented asoperations performed by a data processing apparatus on data stored onone or more computer-readable storage devices or received from othersources. The term “data processing apparatus” encompasses all kinds ofapparatus, devices, and machines for processing data, including by wayof example a programmable processor, a computer, a system on a chip, ormultiple ones, or combinations, of the foregoing. The apparatus can alsoinclude, in addition to hardware, code that creates an executionenvironment for the computer program in question, e.g., code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, a cross-platform runtime environment, avirtual machine, or a combination of one or more of them. The apparatusand execution environment can realize various different computing modelinfrastructures, such as web services, distributed computing and gridcomputing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative orprocedural languages, and it can be deployed in any form, including as astand alone program or as a module, component, subroutine, object, orother unit suitable for use in a computing environment. A computerprogram may, but need not, correspond to a file in a file system. Aprogram can be stored in a portion of a file that holds other programsor data (e.g., one or more scripts stored in a markup languagedocument), in a single file dedicated to the program in question, or inmultiple coordinated files (e.g., files that store one or more modules,sub programs, or portions of code). A computer program can be deployedto be executed on one computer or on multiple computers that are locatedat one site or distributed across multiple sites and interconnected by acommunication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. Processors suitable for the execution of a computerprogram include, by way of example, both general and special purposemicroprocessors, and any one or more processors of any kind of digitalcomputer. Generally, a processor will receive instructions and data froma read only memory or a random access memory or both. The essentialelements of a computer are a processor for performing actions inaccordance with instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data. Devices suitable forstoring computer program instructions and data include all forms ofnonvolatile memory, media and memory devices.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device for displaying information to the user and akeyboard and a pointing device, e.g., a mouse, by which the user canprovide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput. In addition, a computer can interact with a user by sendingdocuments to and receiving documents from a device that is used by theuser; for example, by sending web pages to a web browser on a user'suser device in response to requests received from the web browser.

The computing system can include a user device and servers. A userdevice and server are generally remote from each other and typicallyinteract through a communication network. The relationship of user andserver arises by virtue of computer programs running on the respectivecomputers and having a user-server relationship to each other. In someembodiments, a server transmits data (e.g., an HTML page) to a userdevice (e.g., for purposes of displaying data to and receiving userinput from a user interacting with the user device). Data generated atthe user device (e.g., a result of the user interaction) can be receivedfrom the user device at the server.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments of particular inventions.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. A computer-implemented method implemented in adata processing apparatus, the method comprising: for a power customerlocation that receives power from a power source, the power customerlocation including a plurality of local distribution branches andwherein each local distribution branch includes a plurality ofelectrical loads: receiving reporting data from each of a plurality ofpower management devices, each power management device coupled to arespective one of the electrical loads and providing power managementfor the electrical load; wherein for each power management device thereporting data includes: power characteristics as detected at anelectrical load on the distribution branch; and a time at which thepower characteristics were detected; determining, from the reportingdata, a historical power environment profile for the customer locationthat describes historical power characteristics for each of theelectrical loads on the distribution branches, comprising: determining,for each electrical load: determining, for a plurality of correspondingtimes, an operational state of the electrical load at the correspondingtime, wherein the operational states include a healthy state duringwhich the electrical load is not experience a malfunction and amalfunction state during which the electrical load is experiencing amalfunction; determining, from the operational states at thecorresponding times, transitions from a healthy state to a malfunctionstate; determining, for each transition from a healthy state to amalfunction state, respective sets of power characteristics at thecorresponding times for the transitions, each respective set of powercharacteristics being for a different electrical load; determining,based on the historical power environment profile, a combination ofelectrical loads that results in at least a first electrical loadoperating in a healthy state inducing power-related malfunctions in atleast a second electrical load, comprising: comparing the respectivesets of power characteristics at the corresponding times for thetransitions to each other; and determining that power characteristicsfor the at least one electrical load of a first type while the at leastone electrical load is operating in a healthy state have a positivecorrelation with transitions from a healthy state to a malfunction statefor at least one electrical load of a second type that is different fromthe first type; and generating, in response to the determination, analert that describes the combination of electrical loads.
 2. Thecomputer-implemented method of claim 1, wherein determining acombination of electrical loads comprises determining a combination ofelectrical loads of two or more different types on a distribution branchthat result in at least the one of the electrical loads of the firsttype and operating in the healthy state on the distribution branchinducing power-related malfunctions in the at least one electrical loadof the second type that is different from the first type on thedistribution branch.
 3. The computer-implemented method of claim 1,wherein determining, based on the historical power environment profile,a combination of electrical loads of two or more different types thatresult in at least one of the electrical loads of a first type andoperating in a healthy state inducing power-related malfunctions in atleast one electrical load of a second type that is different from thefirst type comprises: comparing the respective sets of powercharacteristics at the corresponding times for the transitions to eachother; and determining that power characteristics for the at least oneelectrical load of the first type have a positive correlation withtransitions from a healthy state to a malfunction state for the at leastone electrical load of the second type.
 4. The computer-implementedmethod of claim 3, wherein the power characteristics describe one ormore voltage and current conditions for the electrical load.
 5. Thecomputer-implemented method of claim 3, wherein the powercharacteristics describe one or more environmental conditions for theelectrical load.
 6. The computer-implemented method of claim 5, whereinthe environmental conditions describe one or more of temperature andhumidity for the electrical load.
 7. A non-transitory computer readablemedium storing instructions executable by a data processing apparatusand that upon such execution cause the data processing apparatus toperform operations comprising: for a power customer location thatreceives power from a power source, the power customer locationincluding a plurality of local distribution branches and wherein eachlocal distribution branch includes a plurality of electrical loads:receiving reporting data from each of a plurality of power managementdevices, each power management device coupled to a respective one of theelectrical loads and providing power management for the electrical load;wherein for each power management device the reporting data includes:power characteristics as detected at an electrical load on thedistribution branch; and a time at which the power characteristics weredetected; determining, from the reporting data, a historical powerenvironment profile for the customer location that describes historicalpower characteristics for each of the electrical loads on thedistribution branches, comprising: determining, for each electricalload: determining, for a plurality of corresponding times, anoperational state of the electrical load at the corresponding time,wherein the operational states include a healthy state during which theelectrical load is not experience a malfunction and a malfunction stateduring which the electrical load is experiencing a malfunction;determining, from the operational states at the corresponding times,transitions from a healthy state to a malfunction state; determining,for each transition from a healthy state to a malfunction state,respective sets of power characteristics at the corresponding times forthe transitions, each respective set of power characteristics being fora different electrical load; determining, based on the historical powerenvironment profile, a combination of electrical loads that results inat least a first electrical load operating in a healthy state inducingpower-related malfunctions in at least a second electrical load,comprising: comparing the respective sets of power characteristics atthe corresponding times for the transitions to each other; anddetermining that power characteristics for the at least one electricalload of a first type while the at least one electrical load is operatingin a healthy state have a positive correlation with transitions from ahealthy state to a malfunction state for at least one electrical load ofa second type that is different from the first type; and generating, inresponse to the determination, an alert that describes the combinationof electrical loads.
 8. A system, comprising: a data processingapparatus; and a non-transitory computer readable medium storinginstructions executable by the data processing apparatus and that uponsuch execution cause the data processing apparatus to perform operationscomprising: for a power customer location that receives power from apower source, the power customer location including a plurality of localdistribution branches and wherein each local distribution branchincludes a plurality of electrical loads: receiving reporting data fromeach of a plurality of power management devices, each power managementdevice coupled to a respective one of the electrical loads and providingpower management for the electrical load; wherein for each powermanagement device the reporting data includes: power characteristics asdetected at an electrical load on the distribution branch; and a time atwhich the power characteristics were detected; determining, from thereporting data, a historical power environment profile for the customerlocation that describes historical power characteristics for each of theelectrical loads on the distribution branches, comprising: determining,for each electrical load: determining, for a plurality of correspondingtimes, an operational state of the electrical load at the correspondingtime, wherein the operational states include a healthy state duringwhich the electrical load is not experience a malfunction and amalfunction state during which the electrical load is experiencing amalfunction; determining, from the operational states at thecorresponding times, transitions from a healthy state to a malfunctionstate; determining, for each transition from a healthy state to amalfunction state, respective sets of power characteristics at thecorresponding times for the transitions, each respective set of powercharacteristics being for a different electrical load; determining,based on the historical power environment profile, a combination ofelectrical loads that results in at least a first electrical loadoperating in a healthy state inducing power-related malfunctions in atleast a second electrical load, comprising: comparing the respectivesets of power characteristics at the corresponding times for thetransitions to each other; and determining that power characteristicsfor the at least one electrical load of a first type while the at leastone electrical load is operating in a healthy state have a positivecorrelation with transitions from a healthy state to a malfunction statefor at least one electrical load of a second type that is different fromthe first type; and generating, in response to the determination, analert that describes the combination of electrical loads.